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Level 2 / Project 13 - Validation Rule Engine

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Estimated time: 45 minutes

Focus

  • rule evaluation and reason codes

Why this project exists

This project gives you level-appropriate practice in a realistic operations context. Goal: run the baseline, alter behavior, break one assumption, recover safely, and explain the fix.

Run (copy/paste)

Use <repo-root> as the folder containing this repository's README.md.

cd <repo-root>/projects/level-2/13-validation-rule-engine
python project.py data/sample_input.txt
python project.py data/sample_input.txt --verbose
pytest -q

Expected terminal output

Validated 8 records: 4 valid, 4 invalid (50.0% pass rate)
Most common failures: R003: 1, R001: 1, ...
12 passed

Expected artifacts

  • Validation report on stdout
  • Passing tests
  • Updated notes.md

Checkpoint: Baseline code runs and all tests pass. Commit your work before continuing.

Alter it (required) — Extension

  1. Add a new rule type "one_of" that checks if a value is in an allowed list.
  2. Add a --rules flag to load rules from a separate JSON file.
  3. Add a --strict mode that stops validation after the first failure per record.

Break it (required) — Core

  1. Pass a record with a field that is an unexpected type (e.g. age as a list).
  2. Use an invalid regex pattern in a rule — does it crash?
  3. Feed an empty records array — does the pass_rate calculation divide by zero?

Fix it (required) — Core

  1. Guard the regex check against re.error exceptions.
  2. Handle division by zero in pass_rate for empty batches.
  3. Add type checking before range validation.

Checkpoint: All modifications done, tests still pass. Good time to review your changes.

Explain it (teach-back)

  1. Why are rules defined as data instead of hard-coded if/else chains?
  2. How does the dispatch pattern (if rule_type == ...) work?
  3. What is the advantage of returning result dicts instead of printing directly?
  4. Where would a validation rule engine be used in production systems?

Mastery check

You can move on when you can: - add a new rule type without modifying existing rule checks, - explain data-driven design vs code-driven design, - write regex patterns for common validations (email, phone, URL), - describe how this pattern scales to complex validation scenarios.

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